
Reflection AI's Beam: How a 501B MoE Model Learns to Think in Fewer Tokens
Reflection AI's Beam: How a 501B MoE Model Learns to Think in Fewer Tokens When most open-weight model releases compete on raw benchmark scores, Reflection AI took a different angle with Beam: instead of asking "how capable can we make this model?", they asked "how much reasoning compute does a correct answer actually require?" The result is a 501-billion-parameter sparse Mixture-of-Experts model that achieves frontier-level coding and agentic performance while using roughly 3–4× fewer inference



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